Online Gaming

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math

import warnings
warnings.filterwarnings("ignore")

# yahoo finance data 
import yfinance as yf
yf.pdr_override()

# input
# Online Gaming
title = "Online Gaming"
symbols = ['EA', 'NTDOY', 'ESPO', 'IGGGF', 'SCPL', 'TTWO', 'ATVI', 'ZNGA', 'NEXOF', 'CZR', 'YY', 'CHDN', 'MOMO', 'IGT', 'GLUU', 'CYOU', 'SOHU', 'CMC']
start = '2018-01-01'
end = '2020-06-26'

df = pd.DataFrame()
for s in symbols:
    df[s] = yf.download(s,start,end)['Adj Close']

[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed
[*********************100%***********************]  1 of 1 completed

from datetime import datetime
from dateutil import relativedelta

d1 = datetime.strptime(start, "%Y-%m-%d")
d2 = datetime.strptime(end, "%Y-%m-%d")
delta = relativedelta.relativedelta(d2,d1)
print('How many years of investing?')
print('%s years' % delta.years)

How many years of investing?
2 years

number_of_years = delta.years

days = (df.index[-1] - df.index[0]).days
days

905

df.head()

	EA	NTDOY	ESPO	IGGGF	SCPL	TTWO	ATVI	ZNGA	NEXOF	CZR	YY	CHDN	MOMO	IGT	GLUU	CYOU	SOHU	CMC
Date
2018-01-02	109.449997	44.045223	NaN	0.825251	NaN	112.879997	63.055592	3.92	12.058393	12.45	121.459999	78.517570	24.564751	23.521132	3.63	12.266425	45.580002	21.397432
2018-01-03	109.750000	45.043194	NaN	0.825251	NaN	113.879997	64.036087	4.02	12.058393	12.48	119.360001	78.451607	24.602745	23.406864	3.65	12.136746	44.990002	22.835211
2018-01-04	107.190002	46.578537	NaN	0.825251	NaN	114.019997	63.398773	3.89	12.058393	12.90	121.930000	78.039307	25.524164	23.644188	3.67	12.070243	45.240002	23.004358
2018-01-05	112.389999	45.513390	NaN	0.825251	NaN	116.910004	65.075409	3.89	12.058393	12.85	128.100006	79.012321	26.436077	24.408888	3.81	11.930589	46.410000	23.399038
2018-01-08	113.220001	45.647736	NaN	0.825251	NaN	117.370003	65.330338	3.91	12.058393	12.65	129.270004	79.177246	26.540569	24.259468	3.80	12.236500	46.820000	23.756136

df.tail()

	EA	NTDOY	ESPO	IGGGF	SCPL	TTWO	ATVI	ZNGA	NEXOF	CZR	YY	CHDN	MOMO	IGT	GLUU	CYOU	SOHU	CMC
Date
2020-06-19	130.190002	58.590000	50.959999	0.80000	13.96	142.610001	76.580002	9.29	21.980000	12.28	97.230003	133.509995	19.969999	9.08	9.31	NaN	9.38	20.299999
2020-06-22	129.720001	58.500000	51.790001	0.82500	14.25	141.139999	76.120003	9.44	21.980000	12.09	97.129997	135.750000	19.139999	8.91	9.27	NaN	9.33	20.580000
2020-06-23	130.600006	56.820000	52.310001	0.78500	14.37	143.369995	76.449997	9.57	21.980000	12.45	96.980003	141.699997	19.219999	9.47	9.48	NaN	9.68	20.549999
2020-06-24	130.869995	56.439999	51.630001	0.79496	14.54	139.869995	75.580002	9.48	21.980000	12.05	93.629997	132.690002	18.459999	9.07	9.25	NaN	9.02	19.639999
2020-06-25	130.740005	55.930000	51.820000	0.79000	14.66	140.399994	76.209999	9.61	23.549999	11.95	94.370003	133.949997	17.820000	8.78	9.41	NaN	9.38	20.180000

plt.figure(figsize=(12,8))
plt.plot(df)
plt.title(title + ' Closing Price')
plt.legend(labels=df.columns)

<matplotlib.legend.Legend at 0x233687c8160>

# Normalize the data
normalize = (df - df.min())/ (df.max() - df.min())

plt.figure(figsize=(18,12))
plt.plot(normalize)
plt.title(title + ' Stocks Normalize')
plt.legend(labels=normalize.columns)

<matplotlib.legend.Legend at 0x233697cfef0>

stock_rets = df.pct_change().dropna()

plt.figure(figsize=(12,8))
plt.plot(stock_rets)
plt.title(title + ' Stocks Returns')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x23369862898>

plt.figure(figsize=(12,8))
plt.plot(stock_rets.cumsum())
plt.title(title + ' Stocks Returns Cumulative Sum')
plt.legend(labels=stock_rets.columns)

<matplotlib.legend.Legend at 0x233698e9b00>

sns.set(style='ticks')
ax = sns.pairplot(stock_rets, diag_kind='hist')

nplot = len(stock_rets.columns)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

ax = sns.PairGrid(stock_rets)
ax.map_upper(plt.scatter, color='purple')
ax.map_lower(sns.kdeplot, color='blue')
ax.map_diag(plt.hist, bins=30)
for i in range(nplot) :
    for j in range(nplot) :
        ax.axes[i, j].locator_params(axis='x', nbins=6, tight=True)

plt.figure(figsize=(10,10))
corr = stock_rets.corr()

# plot the heatmap
sns.heatmap(corr, 
        xticklabels=corr.columns,
        yticklabels=corr.columns,
            cmap="Reds")

<matplotlib.axes._subplots.AxesSubplot at 0x233687ec208>

# Box plot
stock_rets.plot(kind='box',figsize=(24,8))

<matplotlib.axes._subplots.AxesSubplot at 0x23375f7afd0>

rets = stock_rets.dropna()

plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(),alpha = 0.5)

plt.title('Stocks Risk & Returns')
plt.xlabel('Risk')
plt.ylabel('Expected Returns')
plt.grid(which='major')

for label, x, y in zip(rets.columns, rets.std(), rets.mean()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (50, 50),
        textcoords = 'offset points', ha = 'right', va = 'bottom',
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,rad=-0.3'))

rets = stock_rets.dropna()
area = np.pi*20.0

sns.set(style='darkgrid')
plt.figure(figsize=(16,8))
plt.scatter(rets.std(), rets.mean(), s=area)
plt.xlabel("Risk", fontsize=15)
plt.ylabel("Expected Return", fontsize=15)
plt.title("Return vs. Risk for Stocks", fontsize=20)

for label, x, y in zip(rets.columns, rets.std(), rets.mean()) : 
    plt.annotate(label, xy=(x,y), xytext=(50, 0), textcoords='offset points',
                arrowprops=dict(arrowstyle='-', connectionstyle='bar,angle=180,fraction=-0.2'),
                bbox=dict(boxstyle="round", fc="w"))

rest_rets = rets.corr()
pair_value = rest_rets.abs().unstack()
pair_value.sort_values(ascending = False)

CMC    CMC      1.000000
NEXOF  NEXOF    1.000000
GLUU   GLUU     1.000000
IGT    IGT      1.000000
MOMO   MOMO     1.000000
CHDN   CHDN     1.000000
YY     YY       1.000000
CZR    CZR      1.000000
ZNGA   ZNGA     1.000000
SOHU   SOHU     1.000000
ATVI   ATVI     1.000000
TTWO   TTWO     1.000000
SCPL   SCPL     1.000000
IGGGF  IGGGF    1.000000
ESPO   ESPO     1.000000
NTDOY  NTDOY    1.000000
CYOU   CYOU     1.000000
EA     EA       1.000000
       ATVI     0.769795
ATVI   EA       0.769795
ESPO   EA       0.700928
EA     ESPO     0.700928
ATVI   ESPO     0.700343
ESPO   ATVI     0.700343
TTWO   EA       0.672437
EA     TTWO     0.672437
CMC    CHDN     0.669305
CHDN   CMC      0.669305
IGT    CHDN     0.663151
CHDN   IGT      0.663151
                  ...   
MOMO   IGGGF    0.035994
IGGGF  MOMO     0.035994
CYOU   ZNGA     0.034611
ZNGA   CYOU     0.034611
IGGGF  NTDOY    0.029423
NTDOY  IGGGF    0.029423
IGGGF  CHDN     0.027796
CHDN   IGGGF    0.027796
       NEXOF    0.027594
NEXOF  CHDN     0.027594
CMC    NEXOF    0.023054
NEXOF  CMC      0.023054
SCPL   IGGGF    0.016811
IGGGF  SCPL     0.016811
       CYOU     0.016021
CYOU   IGGGF    0.016021
NEXOF  CZR      0.015009
CZR    NEXOF    0.015009
NEXOF  YY       0.014615
YY     NEXOF    0.014615
NEXOF  EA       0.011146
EA     NEXOF    0.011146
SCPL   NEXOF    0.009949
NEXOF  SCPL     0.009949
IGGGF  GLUU     0.004558
GLUU   IGGGF    0.004558
TTWO   NEXOF    0.004274
NEXOF  TTWO     0.004274
ATVI   NEXOF    0.003669
NEXOF  ATVI     0.003669
Length: 324, dtype: float64

# Normalized Returns Data
Normalized_Value = ((rets[:] - rets[:].min()) /(rets[:].max() - rets[:].min()))
Normalized_Value.head()

	EA	NTDOY	ESPO	IGGGF	SCPL	TTWO	ATVI	ZNGA	NEXOF	CZR	YY	CHDN	MOMO	IGT	GLUU	CYOU	SOHU	CMC
Date
2019-05-06	0.518219	0.409265	0.504491	0.306879	0.369190	0.552440	0.645066	0.534552	0.436324	0.406431	0.400695	0.449707	0.419356	0.477271	0.650554	0.232529	0.419138	0.506156
2019-05-07	0.390781	0.177646	0.396872	0.504326	0.480122	0.470107	0.381810	0.382295	0.436324	0.388646	0.345438	0.417084	0.270575	0.420012	0.295015	0.205221	0.440428	0.494285
2019-05-08	0.511900	0.558324	0.638220	0.454945	0.503106	0.640910	0.552075	0.402307	0.436324	0.404411	0.472605	0.522121	0.525809	0.511637	0.608965	0.226088	0.507522	0.562286
2019-05-09	0.416781	0.365847	0.475347	0.348541	0.477204	0.565225	0.517529	0.367187	0.436324	0.446860	0.483452	0.541619	0.501216	0.489164	0.553264	0.256949	0.583919	0.612975
2019-05-10	0.478751	0.539276	0.668463	0.556262	0.373956	0.608435	0.469833	0.436855	0.436324	0.444881	0.422747	0.518846	0.118767	0.501903	0.561354	0.206170	0.485495	0.598652

Normalized_Value.corr()

	EA	NTDOY	ESPO	IGGGF	SCPL	TTWO	ATVI	ZNGA	NEXOF	CZR	YY	CHDN	MOMO	IGT	GLUU	CYOU	SOHU	CMC
EA	1.000000	0.380182	0.700928	0.157182	0.259232	0.672437	0.769795	0.567288	-0.011146	0.245238	0.400390	0.266890	0.232537	0.153463	0.288126	0.116331	0.310074	0.417896
NTDOY	0.380182	1.000000	0.545280	0.029423	0.266442	0.341888	0.361275	0.340067	0.074306	0.232519	0.230017	0.258081	0.220835	0.189643	0.227081	0.091222	0.200619	0.355022
ESPO	0.700928	0.545280	1.000000	0.188304	0.310329	0.641774	0.700343	0.652942	0.058710	0.415085	0.551762	0.573559	0.400842	0.430467	0.330736	0.110731	0.506717	0.619716
IGGGF	0.157182	0.029423	0.188304	1.000000	-0.016811	0.131509	0.200993	0.070299	-0.042531	-0.051777	0.099707	0.027796	0.035994	0.092622	0.004558	-0.016021	0.042983	0.093465
SCPL	0.259232	0.266442	0.310329	-0.016811	1.000000	0.192145	0.248373	0.240408	0.009949	0.389056	0.080781	0.344550	0.156615	0.316277	0.219720	0.202341	0.240681	0.323571
TTWO	0.672437	0.341888	0.641774	0.131509	0.192145	1.000000	0.661742	0.576547	-0.004274	0.246298	0.354249	0.336688	0.193331	0.189039	0.300207	0.075384	0.310798	0.352125
ATVI	0.769795	0.361275	0.700343	0.200993	0.248373	0.661742	1.000000	0.643923	0.003669	0.187088	0.357106	0.273186	0.222235	0.134534	0.347538	0.140093	0.364271	0.374468
ZNGA	0.567288	0.340067	0.652942	0.070299	0.240408	0.576547	0.643923	1.000000	0.046448	0.227537	0.311999	0.357178	0.221526	0.205244	0.481572	0.034611	0.267029	0.321260
NEXOF	-0.011146	0.074306	0.058710	-0.042531	0.009949	-0.004274	0.003669	0.046448	1.000000	0.015009	-0.014615	0.027594	-0.091048	0.124332	-0.087753	-0.043627	0.037308	0.023054
CZR	0.245238	0.232519	0.415085	-0.051777	0.389056	0.246298	0.187088	0.227537	0.015009	1.000000	0.340210	0.616646	0.349485	0.524989	0.086707	0.061350	0.378445	0.522939
YY	0.400390	0.230017	0.551762	0.099707	0.080781	0.354249	0.357106	0.311999	-0.014615	0.340210	1.000000	0.304584	0.537409	0.365797	0.144579	0.096067	0.555499	0.422024
CHDN	0.266890	0.258081	0.573559	0.027796	0.344550	0.336688	0.273186	0.357178	0.027594	0.616646	0.304584	1.000000	0.327434	0.663151	0.176078	0.070140	0.408144	0.669305
MOMO	0.232537	0.220835	0.400842	0.035994	0.156615	0.193331	0.222235	0.221526	-0.091048	0.349485	0.537409	0.327434	1.000000	0.275994	0.174447	0.128068	0.509014	0.420954
IGT	0.153463	0.189643	0.430467	0.092622	0.316277	0.189039	0.134534	0.205244	0.124332	0.524989	0.365797	0.663151	0.275994	1.000000	0.103231	0.093920	0.330625	0.542689
GLUU	0.288126	0.227081	0.330736	0.004558	0.219720	0.300207	0.347538	0.481572	-0.087753	0.086707	0.144579	0.176078	0.174447	0.103231	1.000000	0.087568	0.094780	0.214551
CYOU	0.116331	0.091222	0.110731	-0.016021	0.202341	0.075384	0.140093	0.034611	-0.043627	0.061350	0.096067	0.070140	0.128068	0.093920	0.087568	1.000000	0.255068	0.129624
SOHU	0.310074	0.200619	0.506717	0.042983	0.240681	0.310798	0.364271	0.267029	0.037308	0.378445	0.555499	0.408144	0.509014	0.330625	0.094780	0.255068	1.000000	0.494771
CMC	0.417896	0.355022	0.619716	0.093465	0.323571	0.352125	0.374468	0.321260	0.023054	0.522939	0.422024	0.669305	0.420954	0.542689	0.214551	0.129624	0.494771	1.000000

normalized_rets = Normalized_Value.corr()
normalized_pair_value = normalized_rets.abs().unstack()
normalized_pair_value.sort_values(ascending = False)

CMC    CMC      1.000000
NEXOF  NEXOF    1.000000
GLUU   GLUU     1.000000
IGT    IGT      1.000000
MOMO   MOMO     1.000000
CHDN   CHDN     1.000000
YY     YY       1.000000
CZR    CZR      1.000000
ZNGA   ZNGA     1.000000
SOHU   SOHU     1.000000
ATVI   ATVI     1.000000
TTWO   TTWO     1.000000
SCPL   SCPL     1.000000
IGGGF  IGGGF    1.000000
ESPO   ESPO     1.000000
NTDOY  NTDOY    1.000000
CYOU   CYOU     1.000000
EA     EA       1.000000
       ATVI     0.769795
ATVI   EA       0.769795
ESPO   EA       0.700928
EA     ESPO     0.700928
ATVI   ESPO     0.700343
ESPO   ATVI     0.700343
TTWO   EA       0.672437
EA     TTWO     0.672437
CMC    CHDN     0.669305
CHDN   CMC      0.669305
IGT    CHDN     0.663151
CHDN   IGT      0.663151
                  ...   
MOMO   IGGGF    0.035994
IGGGF  MOMO     0.035994
CYOU   ZNGA     0.034611
ZNGA   CYOU     0.034611
IGGGF  NTDOY    0.029423
NTDOY  IGGGF    0.029423
IGGGF  CHDN     0.027796
CHDN   IGGGF    0.027796
       NEXOF    0.027594
NEXOF  CHDN     0.027594
CMC    NEXOF    0.023054
NEXOF  CMC      0.023054
SCPL   IGGGF    0.016811
IGGGF  SCPL     0.016811
       CYOU     0.016021
CYOU   IGGGF    0.016021
NEXOF  CZR      0.015009
CZR    NEXOF    0.015009
NEXOF  YY       0.014615
YY     NEXOF    0.014615
NEXOF  EA       0.011146
EA     NEXOF    0.011146
SCPL   NEXOF    0.009949
NEXOF  SCPL     0.009949
IGGGF  GLUU     0.004558
GLUU   IGGGF    0.004558
TTWO   NEXOF    0.004274
NEXOF  TTWO     0.004274
ATVI   NEXOF    0.003669
NEXOF  ATVI     0.003669
Length: 324, dtype: float64

print("Stock returns: ")
print(rets.mean())
print('-' * 50)
print("Stock risks:")
print(rets.std())

Stock returns: 
EA       0.001435
NTDOY    0.001058
ESPO     0.001715
IGGGF    0.000105
SCPL     0.000520
TTWO     0.001412
ATVI     0.001977
ZNGA     0.001905
NEXOF    0.003572
CZR      0.001935
YY       0.001216
CHDN     0.001872
MOMO    -0.001844
IGT      0.000121
GLUU     0.000685
CYOU     0.000899
SOHU    -0.001541
CMC      0.001184
dtype: float64
--------------------------------------------------
Stock risks:
EA       0.021852
NTDOY    0.018443
ESPO     0.017587
IGGGF    0.046357
SCPL     0.036414
TTWO     0.024151
ATVI     0.024156
ZNGA     0.024281
NEXOF    0.047753
CZR      0.046313
YY       0.037137
CHDN     0.042783
MOMO     0.032884
IGT      0.057194
GLUU     0.047045
CYOU     0.033995
SOHU     0.045675
CMC      0.035453
dtype: float64

table = pd.DataFrame()
table['Returns'] = rets.mean()
table['Risk'] = rets.std()
table.sort_values(by='Returns')

	Returns	Risk
MOMO	-0.001844	0.032884
SOHU	-0.001541	0.045675
IGGGF	0.000105	0.046357
IGT	0.000121	0.057194
SCPL	0.000520	0.036414
GLUU	0.000685	0.047045
CYOU	0.000899	0.033995
NTDOY	0.001058	0.018443
CMC	0.001184	0.035453
YY	0.001216	0.037137
TTWO	0.001412	0.024151
EA	0.001435	0.021852
ESPO	0.001715	0.017587
CHDN	0.001872	0.042783
ZNGA	0.001905	0.024281
CZR	0.001935	0.046313
ATVI	0.001977	0.024156
NEXOF	0.003572	0.047753

table.sort_values(by='Risk')

	Returns	Risk
ESPO	0.001715	0.017587
NTDOY	0.001058	0.018443
EA	0.001435	0.021852
TTWO	0.001412	0.024151
ATVI	0.001977	0.024156
ZNGA	0.001905	0.024281
MOMO	-0.001844	0.032884
CYOU	0.000899	0.033995
CMC	0.001184	0.035453
SCPL	0.000520	0.036414
YY	0.001216	0.037137
CHDN	0.001872	0.042783
SOHU	-0.001541	0.045675
CZR	0.001935	0.046313
IGGGF	0.000105	0.046357
GLUU	0.000685	0.047045
NEXOF	0.003572	0.047753
IGT	0.000121	0.057194

rf = 0.01
table['Sharpe Ratio'] = (table['Returns'] - rf) / table['Risk']
table

	Returns	Risk	Sharpe Ratio
EA	0.001435	0.021852	-0.391945
NTDOY	0.001058	0.018443	-0.484861
ESPO	0.001715	0.017587	-0.471083
IGGGF	0.000105	0.046357	-0.213455
SCPL	0.000520	0.036414	-0.260332
TTWO	0.001412	0.024151	-0.355602
ATVI	0.001977	0.024156	-0.332144
ZNGA	0.001905	0.024281	-0.333407
NEXOF	0.003572	0.047753	-0.134616
CZR	0.001935	0.046313	-0.174138
YY	0.001216	0.037137	-0.236529
CHDN	0.001872	0.042783	-0.189973
MOMO	-0.001844	0.032884	-0.360181
IGT	0.000121	0.057194	-0.172724
GLUU	0.000685	0.047045	-0.198003
CYOU	0.000899	0.033995	-0.267723
SOHU	-0.001541	0.045675	-0.252671
CMC	0.001184	0.035453	-0.248680

table['Max Returns'] = rets.max()

table['Min Returns'] = rets.min()

table['Median Returns'] = rets.median()

total_return = stock_rets[-1:].transpose()
table['Total Return'] = 100 * total_return
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return
EA	0.001435	0.021852	-0.391945	0.106649	-0.086441	0.001675	-0.099327
NTDOY	0.001058	0.018443	-0.484861	0.080611	-0.062846	-0.000035	-0.903612
ESPO	0.001715	0.017587	-0.471083	0.068273	-0.088411	0.001570	0.368000
IGGGF	0.000105	0.046357	-0.213455	0.231884	-0.193548	0.000000	-0.623931
SCPL	0.000520	0.036414	-0.260332	0.205047	-0.164706	0.000000	0.825309
TTWO	0.001412	0.024151	-0.355602	0.096555	-0.118522	0.001277	0.378922
ATVI	0.001977	0.024156	-0.332144	0.091734	-0.106369	0.002143	0.833550
ZNGA	0.001905	0.024281	-0.333407	0.133829	-0.103817	0.000000	1.371309
NEXOF	0.003572	0.047753	-0.134616	0.291875	-0.225931	0.000000	7.142856
CZR	0.001935	0.046313	-0.174138	0.426136	-0.309804	0.001477	-0.829879
YY	0.001216	0.037137	-0.236529	0.159496	-0.158164	-0.001029	0.790351
CHDN	0.001872	0.042783	-0.189973	0.210870	-0.220975	0.002965	0.949578
MOMO	-0.001844	0.032884	-0.360181	0.097575	-0.129826	-0.001461	-3.466952
IGT	0.000121	0.057194	-0.172724	0.343471	-0.334524	0.000000	-3.197354
GLUU	0.000685	0.047045	-0.198003	0.255814	-0.364954	-0.001397	1.729728
CYOU	0.000899	0.033995	-0.267723	0.493243	-0.143426	0.000000	0.000000
SOHU	-0.001541	0.045675	-0.252671	0.255072	-0.268412	-0.003699	3.991127
CMC	0.001184	0.035453	-0.248680	0.121392	-0.173639	0.001536	2.749496

table['Average Return Days'] = (1 + total_return)**(1 / days) - 1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days
EA	0.001435	0.021852	-0.391945	0.106649	-0.086441	0.001675	-0.099327	-0.000001
NTDOY	0.001058	0.018443	-0.484861	0.080611	-0.062846	-0.000035	-0.903612	-0.000010
ESPO	0.001715	0.017587	-0.471083	0.068273	-0.088411	0.001570	0.368000	0.000004
IGGGF	0.000105	0.046357	-0.213455	0.231884	-0.193548	0.000000	-0.623931	-0.000007
SCPL	0.000520	0.036414	-0.260332	0.205047	-0.164706	0.000000	0.825309	0.000009
TTWO	0.001412	0.024151	-0.355602	0.096555	-0.118522	0.001277	0.378922	0.000004
ATVI	0.001977	0.024156	-0.332144	0.091734	-0.106369	0.002143	0.833550	0.000009
ZNGA	0.001905	0.024281	-0.333407	0.133829	-0.103817	0.000000	1.371309	0.000015
NEXOF	0.003572	0.047753	-0.134616	0.291875	-0.225931	0.000000	7.142856	0.000076
CZR	0.001935	0.046313	-0.174138	0.426136	-0.309804	0.001477	-0.829879	-0.000009
YY	0.001216	0.037137	-0.236529	0.159496	-0.158164	-0.001029	0.790351	0.000009
CHDN	0.001872	0.042783	-0.189973	0.210870	-0.220975	0.002965	0.949578	0.000010
MOMO	-0.001844	0.032884	-0.360181	0.097575	-0.129826	-0.001461	-3.466952	-0.000039
IGT	0.000121	0.057194	-0.172724	0.343471	-0.334524	0.000000	-3.197354	-0.000036
GLUU	0.000685	0.047045	-0.198003	0.255814	-0.364954	-0.001397	1.729728	0.000019
CYOU	0.000899	0.033995	-0.267723	0.493243	-0.143426	0.000000	0.000000	0.000000
SOHU	-0.001541	0.045675	-0.252671	0.255072	-0.268412	-0.003699	3.991127	0.000043
CMC	0.001184	0.035453	-0.248680	0.121392	-0.173639	0.001536	2.749496	0.000030

initial_value = df.iloc[0]
ending_value = df.iloc[-1]
table['CAGR'] = ((ending_value / initial_value) ** (252.0 / days)) -1
table

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
EA	0.001435	0.021852	-0.391945	0.106649	-0.086441	0.001675	-0.099327	-0.000001	0.050738
NTDOY	0.001058	0.018443	-0.484861	0.080611	-0.062846	-0.000035	-0.903612	-0.000010	0.068780
ESPO	0.001715	0.017587	-0.471083	0.068273	-0.088411	0.001570	0.368000	0.000004	NaN
IGGGF	0.000105	0.046357	-0.213455	0.231884	-0.193548	0.000000	-0.623931	-0.000007	-0.012082
SCPL	0.000520	0.036414	-0.260332	0.205047	-0.164706	0.000000	0.825309	0.000009	NaN
TTWO	0.001412	0.024151	-0.355602	0.096555	-0.118522	0.001277	0.378922	0.000004	0.062633
ATVI	0.001977	0.024156	-0.332144	0.091734	-0.106369	0.002143	0.833550	0.000009	0.054177
ZNGA	0.001905	0.024281	-0.333407	0.133829	-0.103817	0.000000	1.371309	0.000015	0.283630
NEXOF	0.003572	0.047753	-0.134616	0.291875	-0.225931	0.000000	7.142856	0.000076	0.204888
CZR	0.001935	0.046313	-0.174138	0.426136	-0.309804	0.001477	-0.829879	-0.000009	-0.011349
YY	0.001216	0.037137	-0.236529	0.159496	-0.158164	-0.001029	0.790351	0.000009	-0.067859
CHDN	0.001872	0.042783	-0.189973	0.210870	-0.220975	0.002965	0.949578	0.000010	0.160364
MOMO	-0.001844	0.032884	-0.360181	0.097575	-0.129826	-0.001461	-3.466952	-0.000039	-0.085503
IGT	0.000121	0.057194	-0.172724	0.343471	-0.334524	0.000000	-3.197354	-0.000036	-0.239967
GLUU	0.000685	0.047045	-0.198003	0.255814	-0.364954	-0.001397	1.729728	0.000019	0.303741
CYOU	0.000899	0.033995	-0.267723	0.493243	-0.143426	0.000000	0.000000	0.000000	NaN
SOHU	-0.001541	0.045675	-0.252671	0.255072	-0.268412	-0.003699	3.991127	0.000043	-0.356095
CMC	0.001184	0.035453	-0.248680	0.121392	-0.173639	0.001536	2.749496	0.000030	-0.016179

table.sort_values(by='Average Return Days')

	Returns	Risk	Sharpe Ratio	Max Returns	Min Returns	Median Returns	Total Return	Average Return Days	CAGR
MOMO	-0.001844	0.032884	-0.360181	0.097575	-0.129826	-0.001461	-3.466952	-0.000039	-0.085503
IGT	0.000121	0.057194	-0.172724	0.343471	-0.334524	0.000000	-3.197354	-0.000036	-0.239967
NTDOY	0.001058	0.018443	-0.484861	0.080611	-0.062846	-0.000035	-0.903612	-0.000010	0.068780
CZR	0.001935	0.046313	-0.174138	0.426136	-0.309804	0.001477	-0.829879	-0.000009	-0.011349
IGGGF	0.000105	0.046357	-0.213455	0.231884	-0.193548	0.000000	-0.623931	-0.000007	-0.012082
EA	0.001435	0.021852	-0.391945	0.106649	-0.086441	0.001675	-0.099327	-0.000001	0.050738
CYOU	0.000899	0.033995	-0.267723	0.493243	-0.143426	0.000000	0.000000	0.000000	NaN
ESPO	0.001715	0.017587	-0.471083	0.068273	-0.088411	0.001570	0.368000	0.000004	NaN
TTWO	0.001412	0.024151	-0.355602	0.096555	-0.118522	0.001277	0.378922	0.000004	0.062633
YY	0.001216	0.037137	-0.236529	0.159496	-0.158164	-0.001029	0.790351	0.000009	-0.067859
SCPL	0.000520	0.036414	-0.260332	0.205047	-0.164706	0.000000	0.825309	0.000009	NaN
ATVI	0.001977	0.024156	-0.332144	0.091734	-0.106369	0.002143	0.833550	0.000009	0.054177
CHDN	0.001872	0.042783	-0.189973	0.210870	-0.220975	0.002965	0.949578	0.000010	0.160364
ZNGA	0.001905	0.024281	-0.333407	0.133829	-0.103817	0.000000	1.371309	0.000015	0.283630
GLUU	0.000685	0.047045	-0.198003	0.255814	-0.364954	-0.001397	1.729728	0.000019	0.303741
CMC	0.001184	0.035453	-0.248680	0.121392	-0.173639	0.001536	2.749496	0.000030	-0.016179
SOHU	-0.001541	0.045675	-0.252671	0.255072	-0.268412	-0.003699	3.991127	0.000043	-0.356095
NEXOF	0.003572	0.047753	-0.134616	0.291875	-0.225931	0.000000	7.142856	0.000076	0.204888